High-energy brain dynamics during anesthesia-induced unconsciousness
| Autor: | Riehl, James R., Palanca, Ben J., Ching, ShiNung |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
High energy Consciousness media_common.quotation_subject General anesthesia Network dynamics lcsh:RC321-571 Functional connectivity 03 medical and health sciences 0302 clinical medicine Artificial Intelligence medicine Free energy lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry media_common Brain network Resting state fMRI Research Applied Mathematics General Neuroscience Unconsciousness Computer Science Applications 030104 developmental biology Dynamics (music) Anesthesia medicine.symptom Psychology Neuroscience 030217 neurology & neurosurgery Resting-state networks |
| Zdroj: | Network Neuroscience, Vol 1, Iss 4, Pp 431-445 (2017) Network Neuroscience (Cambridge, Mass.) |
| ISSN: | 2472-1751 |
| DOI: | 10.1162/netn_a_00023 |
| Popis: | Characterizing anesthesia-induced alterations to brain network dynamics provides a powerful framework to understand the neural mechanisms of unconsciousness. To this end, increased attention has been directed at how anesthetic drugs alter the functional connectivity between brain regions as defined through neuroimaging. However, the effects of anesthesia on temporal dynamics at functional network scales is less well understood. Here, we examine such dynamics in view of the free-energy principle, which postulates that brain dynamics tend to promote lower energy (more organized) states. We specifically engaged the hypothesis that such low-energy states play an important role in maintaining conscious awareness. To investigate this hypothesis, we analyzed resting-state BOLD fMRI data from human volunteers during wakefulness and under sevoflurane general anesthesia. Our approach, which extends an idea previously used in the characterization of neuron-scale populations, involves thresholding the BOLD time series and using a normalized Hamiltonian energy function derived from the Ising model. Our major finding is that the brain spends significantly more time in lower energy states during eyes-closed wakefulness than during general anesthesia. This effect is especially pronounced in networks thought to be critical for maintaining awareness, suggesting a crucial cognitive role for both the structure and the dynamical landscape of these networks. Author Summary We show that activity in the human brain, as captured by functional magnetic resonance imaging (fMRI), is more organized during wakefulness than during general anesthesia. This increased organization corresponds to a decrease in a statistical-physics-inspired energy measure among brain regions of shared functional specialization (resting-state networks) that have putative roles in conscious awareness and attention. Characterizing the energy distributions in this way reveals significant changes in the dynamics of brain activity in different states of consciousness, insights that are not observable in the average functional connectivity data alone. Our results indicate that the ability of brain networks to sustain stable representations, via their dynamics, may be crucial for consciousness and cognition. |
| Databáze: | OpenAIRE |
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